Enhancing hull form design for robust efficiency: A data-enhanced simulation-based design approach

Abstract

This paper presents a design approach that integrates machine learning techniques with traditional physics based simulations/models to enhance the ship design process with robust efficiency. While generative machine learning methods, which can directly produce design outputs such as the 3D hull form, have the potential to transform the design strategy, ship design inherently involves a decision-making process that requires consensus among stakeholders based on a foundation in physics-based simulations/models. This paper proposes a practical design strategy that positions physics-based simulations/models at the core of the design process, augmented by data-driven models. The paper first classifies hybrid types of the two models and integrates them into a practical design process. Finally, it demonstrates the effectiveness of the proposed design approach by showcasing the impact of data circulation, which accumulates and reinforces data in day-to-day design operations, on improving design outcomes.